Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer
D Fried1,2*, S Tucker1 , S Zhou1 , Z Liao1 , O Mawlawi1 , G Ibbott1 , L Court11 UT MD Anderson Cancer Center, Houston, TX 2 The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX
PresentationsMO-A-BRD-4 Monday 7:30AM - 9:30AM Room: Ballroom D
Purpose: To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non-small cell lung cancer (NSCLC).
Methods: We retrospectively reviewed 91 patients with stage III NSCLC treated with definitive chemoradiation. All patients underwent a pretreatment diagnostic contrast enhanced CT (CE-CT) followed by a 4D-CT for treatment simulation. We used the average (average-CT) and expiratory (T50-CT) images from the 4D-CT along with the CE-CT for texture extraction. Histogram, gradient, co-occurrence, gray-tone difference, and filtration based techniques were used for texture feature extraction. Penalized Cox regression implementing cross-validation was used for covariate selection and modeling. Models incorporating texture features from the 3 image types and CPFs were compared to models incorporating CPFs alone for overall survival (OS), local-regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan-Meier curves were generated using leave-one-out cross-validation. Patients were stratified based on their predicted outcome being above/below the median. Reproducibility of texture features was evaluated using test-retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test-retest scans to our original models and determined the classification accuracy.
Results: Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone in terms of OS (p=0.046), LRC (p=0.01), and FFDM (p=0.005). The average CCC was 0.89, 0.91, and 0.67 for texture features extracted from the average-CT, T50-CT, and CE-CT, respectively. Incorporating reproducibility within our models yielded 80.4 (SD=3.7), 78.3 (SD=4.0), and 78.8 (SD=3.9) percent classification accuracy in terms of OS, LRC, and FFDM, respectively.
Conclusions: Pretreatment tumor texture may provide prognostic information beyond what is obtained from CPFs. Reproducibility of CE-CT appears inferior to average-CT and T50-CT; however model classification accuracy rates of approximately 80% were still achieved.